Using the final parameter values from each chain
Fig. 1 Model calibration to incident cases (A) and incident deaths (B) reported by JHU CSSE for each state at 0.55% IFR assumptions, summed over entire country. (C) [Optional] Model comparison to hospitalizations (not fit). Shaded areas show 95% confidence intervals based on 4independent inference runs and black points/lines indicate data reported by JHU CSSE.
(for subset of states only now)
Fig. 2 Calibration of estimated incident cases and deaths to reported data from JHU CSSE, and validation of estimates for occupied hospital beds when compared to CDPH data. Here, modeled cases are calculated as a percent of modeled infection that is fit to county data. Black points represent actual data, lines represent means and shading represents the 95% prediction interval for each scenario at 0.55% IFR and 0.5% assumptions. Note that JHU CSSE data were reported as daily cumulative cases and deaths. In this figure, daily cumulative case counts were differenced in order to report the incident cases and deaths. In comparing the actual and modeled data, we emphasize that limited testing and reporting delays may affect the quality of the reported case data early on in the outbreak.
Fig. 3 GeoID-specific log-likelihood values by MCMC step. Here ‘chimeric’ values are the likelihood for accepted parameters in the chimeric likelihood, and ‘global’ values are the likelihood values for the proposed parameters of the chimeric likelihood, which are recorded in the global likelihood files. These two likelihood values are equivalent only at steps where the chimeric likelihood was accepted for that GeoID.**
Fig. 4 Total log-likelihood values by MCMC step (summed over all GeoIDs). Here ‘chimeric’ values are the total likelihood for accepted parameters in the chimeric likelihood, and ‘global’ values are the likelihood values for the proposed parameters of the chimeric likelihood, which are recorded in the global likelihood files. The chimeric (accepted) likelihood is always higher since acceptance decisions are made on a geoID-by-geoID level, and only accepted for GeoIDs where the acceptance would improve the geoID-specific likelihood. These two likelihood values would only be equivalent at steps where the chimeric likelihood was accepted for every single GeoID.**
Fig. 5 Acceptance rate of proposed parameters by MCMC step for each state (‘chimeric’ values) along with the global acceptance rate. Acceptance rate is averaged over all previous steps. **
Fig. 6 Acceptance rate of proposed parameters by MCMC step for each state (‘chimeric’ values) along with the global acceptance rate. Acceptance rate is averaged over the previous10steps**
Fig. 7 Cumulative number of acceptances of proposed parameters by MCMC step for each state (‘chimeric’ values) along with the global acceptance rate**
Note: For these runs, these parameters are the same for all states and are fixed, not estimated, so nothing to plot
Fig. 8 SEIR parameters by MCMC step (‘chimeric’ values)**
Fig. 9 Inferred outcome parameter values by MCMC step for each GeoID.’Global’ values are the proposed parameters at each step, and ‘Chimeric’ values are the parameters accepted at the GeoID-level in the chimeric likelihood **
Fig. 10 Inferred paramter values by MCMC step for each GeoID.’Global’ values are the proposed parameters at each step, and ‘Chimeric’ values are the parameters accepted at the GeoID-level in the chimeric likelihood **
This section might be blank if no outcome parameters were fit
Fig. 11 Inferred paramter values by MCMC step for each GeoID.’Global’ values are the proposed parameters at each step, and ‘Chimeric’ values are the parameters accepted at the GeoID-level in the chimeric likelihood **
Don’t have any plots developed yet for seeding values
Correlations are for within a particular MCMC chain, treating each iteration as a sample
## [1] "California"
## [1] "Florida"
## [1] "Illinois"
## [1] "NewYork"
## [1] "Ohio"
## [1] "Texas"
## [1] "Washington"